An Analysis of Credit Scoring for Agricultural Loans

Appropriate credit scoring assessment assists financial institutions on loan pricing, determining amount of loan, loan risk management, reduction of default risk and increase in debt repayment. The purpose of this study is to estimate a credit scoring model for the agricultural loans in Kohgiloye & Bovair Ahmad Province. The logistic regression with the two estimation methods (classic and Bayesian) are used to construct the credit scoring models as well as to predict the borrower’s creditworthiness and default risk. Furthermore, Bayesian method was compared with the classical estimation methods. The Limdep and MLwiN softwares are used to estimate models by Classic and Bayesian approaches, respectively. Data were collected from 110 farmers in Kohgiloye & Bovair Ahmad Province in 2007.   Results of the Bayesian method indicated that variables such as education, value of assets and age of farmers have positive effects, whereas borrowing from others; loan type, total debet to assets ratio and the duration of bank-borrower relationship have negative effect as important factors in determining the creditworthiness of the borrowers. The results also show that a higher value of assets implies a higher creditworthiness and a higher probability of a good loan. However, the negative sign found on the duration of bankborrower relationship, which contradict with the hypothesized sign, suggest that the borrower who has a longer relationship with the bank has a higher probability to default on debt repaymen.   The overall prediction accuracy of the Bayesian credit scoring models is 90.91% and 89.91% in-sample and out-of-sample forecast, respectively, and is higher than the classic model on out-of-sample forecast. Thus, when the expected loss of misclassification are computed and compared, the results indicate that the misclassification cost of the Bayesian method is the best credit scoring model with the lowest misclassification costs. In summary, the empirical results in this study support the use of the Bayesian method in classifying and screening agricultural loan applications. JEL Classification: C11, C25, E5, Q14